2 research outputs found
CIRCLE: Capture In Rich Contextual Environments
Synthesizing 3D human motion in a contextual, ecological environment is
important for simulating realistic activities people perform in the real world.
However, conventional optics-based motion capture systems are not suited for
simultaneously capturing human movements and complex scenes. The lack of rich
contextual 3D human motion datasets presents a roadblock to creating
high-quality generative human motion models. We propose a novel motion
acquisition system in which the actor perceives and operates in a highly
contextual virtual world while being motion captured in the real world. Our
system enables rapid collection of high-quality human motion in highly diverse
scenes, without the concern of occlusion or the need for physical scene
construction in the real world. We present CIRCLE, a dataset containing 10
hours of full-body reaching motion from 5 subjects across nine scenes, paired
with ego-centric information of the environment represented in various forms,
such as RGBD videos. We use this dataset to train a model that generates human
motion conditioned on scene information. Leveraging our dataset, the model
learns to use ego-centric scene information to achieve nontrivial reaching
tasks in the context of complex 3D scenes. To download the data please visit
https://stanford-tml.github.io/circle_dataset/